Patent application title: System and method for stimulating conciousness

Abstract:

A simulated consciousness method (10) for an improved human/computer
interface. A computer system (12) is programmed to have a Digital Life
Form (32) possessing a plurality of attributes (65). A plurality of
actions (64) taken relative to objects (60) in the environment (30)
contribute to simulated feelings (76) which ultimately control the
viability of the Digital Life Form (32). When there are not sufficient
energy packets (66) to sustain the Digital Life Form (32) then simulated
death 52 results. Therefore, only actions (64) which contribute to the
viability of the Digital Life Form (32) are repeated in the long-run.
Some of those actions (64) include perception of reality, concept
formation, and natural language processing.

Claims:

1. A computer generated entity, comprising: a plurality of attributes,
wherein at least one such attribute defines the vitality of the entity;
and a plurality of actions, at least one of which will affect the
vitality of the entity; and wherein said actions simulate actions by the
entity on objects in an environment, each of said objects including at
least one discernable characteristic; the environment is a computer
generated simulated environment; and the computer generated entity
identifies subsequently encountered objects by comparing discerned
characteristics of the subsequently encountered objects with at least one
percept, each percept identifying discernable characteristics of a
respective one of said objects.

2. The computer generated entity of claim 1, wherein: simulated death
occurs when the actions result in a reduction of vitality below a preset
level; and the computer generated entity chooses such actions so as to
avoid the simulated death.

3. The computer generated entity of claim 1, wherein: vitality level is
determined by a quantity of energy packets.

4. A computer interface, comprising: a digital life form having a
plurality of attributes, including at least one attribute indicative of
the vitality of the digital life form; a plurality of actions which may
be accomplished by the digital life form; and a selection criteria for
selecting from said plurality of actions; and wherein repeated selection
of actions which do not contribute to the vitality of the digital life
form will result in the simulated death of the digital life form; said
digital life form perceives a plurality of objects in an environment;
said objects are identified by the digital life form according to
percepts, each percept identifying perceivable characteristics of a
respective one of said objects; said actions are primarily selected to
keep the digital life form alive.

5. The computer interface of claim 4, wherein: said actions are taken to
optimize at least one of a plurality of simulated feelings.

6. The computer interface of claim 5, wherein: at least one of the
simulated feelings is a feeling of fullness.

7. The computer interface of claim 6, wherein: the feeling of fullness is
represented by a quantity of energy packets.

8. A computer program product comprising a computer usable medium having
a computer readable program code embodied thereon configured to operate
on a computer, comprising: code to cause the computer to keep track of a
list of attributes of a digital life form; code for causing the digital
life form to perceive objects and perceivable characteristics of said
objects; code for causing the digital life form to formulate concepts
based upon perceived characteristics of at least one object; and code to
cause the computer to cause the digital life form to take actions based
upon the concepts.

9. The computer program product of claim 8, wherein: at least some of the
concepts are defined by a human tutor.

10. The computer program product of claim 8, wherein: said actions are
selected from a list of actions programmed into the computer.

11. The computer program product of claim 8, wherein: at least one
consequence of the selection of said actions is the termination of the
digital life form.

12. The computer program product of claim 8, wherein: at least one of the
attributes of the digital life form is a simulated feeling.

13. A method for creating a digital life form, comprising: defining a
digital life form; providing access for the digital life form to an
environment; defining a plurality of potential actions for the digital
life form; providing objects in the environment, the objects including
perceivable characteristics; providing the digital life form with the
ability to form percepts based on the perceivable characteristics of
objects; and providing the digital life form with the ability to select
from said plurality of potential actions based, at least in part, on the
percepts.

14. The method of claim 13, further comprising: providing consequences to
the digital life form for such actions; and wherein the digital life form
selects from said plurality of potential actions in order to avoid
certain of the consequences.

15. The method of claim 14, wherein: said digital life form includes a
plurality of attributes.

16. The method of claim 14, wherein: said environment is a computer
generated simulated environment.

17. The method of claim 14, wherein: at least one of said actions
includes EAT.

18. The method of claim 17, wherein: EAT is defined as assimilating
energy packets to increase the vitality of said digital life form.

19. The method of claim 14, wherein: at least one consequences of said
actions is the simulated death of said digital life form.

20. The method of claim 13, and further including: providing a strategy
for selecting from said plurality of actions.

21. A method for forming concepts in a Digital Life Form, comprising:
forming percepts based on perceived characteristics of objects; and using
said perceived characteristics to form concepts.

22. The method of claim 21, wherein: concepts are compared to form other
concepts in hierarchical conceptual chains.

24. The method of claim 23, wherein: natural language words are
associated into phrases, the meaning of which is the union of the
concepts and one or more conceptual chains that ultimately connect all
the concepts in said conceptual chains to the associated percepts.

Description:

RELATED APPLICATIONS

[0001] This application is a continuation of co-pending U.S. patent
application Ser. No. 12/380,474, filed Feb. 27, 2009 by the same inventor
(now U.S. Pat. No. 7,849,026 issued Dec. 7, 2010), which is a
continuation of U.S. patent application Ser. No. 11/294,622, filed on
Dec. 5, 2005 by the same inventor (now U.S. Pat. No. 7,499,893 issued
Mar. 3, 2009), which is a continuation-in-part of U.S. patent application
Ser. No. 09/802,505, filed Mar. 8, 2001 by the same inventor, all of
which are incorporated herein by reference in their entireties.

COPYRIGHT NOTICE

[0002] A portion of the disclosure of the patent document contains
material which is subject to copyright protection. The owner has no
objection to the facsimile reproduction by any one of the patent
disclosure, as it appears in the Patent and Trademark Office patent files
of records of any country, but otherwise reserves all rights whatsoever.

TECHNICAL FIELD

[0003] The present invention relates to the field of software for
computers and related devices, and more particularly to a method for
causing a computer or other such device to interact with human beings as
though the device has human like consciousness. The predominant current
usage of the present inventive method for simulating consciousness is in
the improvement of communication in human/machine interaction.

BACKGROUND ART

[0004] It is known in the art to cause a computer to emulate certain
functions that are traditionally associated with human behavior. For
example, efforts at artificial intelligence ("AI") generally attempt to
provide knowledge in response to inquiries. However, known AI systems
merely respond with information that has been programmed into them. That
is, a machine programmed with an AI program merely responds in the manner
in which its human programmers provided for when the program was written.

[0005] Experiments in the field of artificial life ("AL") attempt to cause
a machine to function or respond to external stimuli in a manner
generally associated with a living organism. While such experiments are
providing a foundation for future useful devices, the machine/human
interface is not much enhanced by the present state of the art in this
field.

[0006] It is recognized in the field that it would be valuable to have a
computer which does not respond in some preprogrammed automatic manner.
Genetic algorithms have been devised which attempt to get around this
problem by emulating or recapitulating evolution, in the hope that
eventually intelligence will emerge. Neural networks have attempted to do
something similar by emulating the function of neurons in higher life
forms. While it is possible that these methods might eventually help to
solve some aspect of the problem, there has not yet been any useful
benefit derived from such experiments.

[0007] It would be beneficial to have a machine/human interface which
approaches the flexibility of a human/human interface. However, all known
efforts in the field have been limited to either providing a particular
preprogrammed response to an inquiry, or else have not provided a useful
interface between a user and the machine.

DISCLOSURE OF INVENTION

[0008] Accordingly, it is an object of the present invention to provide a
machine/human interface which reacts to stimuli in a manner generally
associated with an animal or a human being.

[0009] It is still another object of the present invention to provide a
machine which simulates consciousness.

[0010] It is yet another object of the present invention to provide a
computer program which will cause a computer to develop a simulated
consciousness.

[0011] It is still another object of the present invention to provide a
method and apparatus for interfacing with a human being as though said
apparatus possesses consciousness.

[0012] It is yet another object of the present invention to provide a
method and apparatus for causing a machine to appear to possess
consciousness.

[0013] It is still another object of the present invention to provide a
method and apparatus for improving a computer/user interface.

[0014] It is yet another object of the present invention to provide an
improved computer/user interface.

[0015] Briefly, a known embodiment of the present invention is a computer
program which establishes goal directed behavior. A computer is
programmed to define actions which can either increase or decrease
simulated happiness scores and which can result either in the continued
existence of a simulated life form or else the demise thereof. Only
actions which tend to perpetuate the simulated life will be repeated in
the long run. In this manner, a Digital Life Form will be goal directed
and will, therefore, act in a manner much as though it is alive and has
actual consciousness. The Digital Life Form can exist entirely within a
computer program for simulation purposes, or can be tied to the "real
world" using sensors, and the like, for practical applications.

[0016] The Digital Life Form, thereby, acts as a teleological agent. An
advantage of the complexity of teleological agents is that they can find
ways to do tasks for which they were not programmed.

[0017] According to the present invention, simulated consciousness is a
series of discrete causal steps performed by program methods that repeat
or cycle operations which a programmer turns into a process by putting
them into a loop internal to the Digital Life Form, in order to simulate
its life and consciousness. The program continuously cycles through these
several program methods, thus effecting the simulation. It is an
important aspect of the invention that while some of the behaviors of the
Digital Life Form are preprogrammed, others are emergent behaviors. That
is, the behaviors emerge from the interaction of the Digital Life Form
with its environment and its own previous actions. Emergent behaviors are
not necessarily predictable from the program code because the environment
is not necessarily predictable. The process steps to simulate
consciousness run in a subsystem layer above those of the Digital Life
Form's simulated life processes, and the program methods that implement
them are to cause the Digital Life Form to perceive its environment,
evaluate objects therein, select an action, act, and record the action
and results thereof to memory. Such action is repeated ad infinitum so
long as the Digital Life Form remains "alive " and, as with biological
life forms, the action may follow any of a variety of paths because the
circumstances in the Digital Life Form's environment are not necessarily
predictable. The result is a very realistic simulation.

[0018] An advantage of the present invention is that a machine can
interface with a human being in a manner generally associated with a
human to human interaction.

[0019] A further advantage of the present invention is that it is easier
for a human to interface with and use a computer.

[0020] Yet another advantage of the present invention is that a computer
can be caused to develop a simulated consciousness with only a minimal
amount of programming.

[0021] Still another advantage of the present invention is that it will be
easier and more natural to use a computer or computerized machine.

[0022] Yet another advantage of the present invention is that it will be
readily implemented using available computer hardware and input/output
devices.

[0023] These and other objects and advantages of the present invention
will become clear to those skilled in the art in view of the description
of modes of carrying out the invention, and the industrial applicability
thereof, as described herein and as illustrated in the several figures of
the drawing. The objects and advantages listed are not an exhaustive list
of all possible advantages of the invention. Moreover, it will be
possible to practice the invention even where one or more of the intended
objects and/or advantages might be absent or not required in the
application.

[0024] Further, those skilled in the art will recognize that various
embodiments of the present invention may achieve one or more, but not
necessarily all, of the above described objects and advantages.
Accordingly, the listed advantages are not essential elements of the
present invention, and should not be construed as limitations.

BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG. 1 is a flow diagram depicting an embodiment of a simulated
awareness method, according to the present invention;

[0026] FIG. 2 is a diagrammatic view of a general purpose computer system
such as may be used for practicing the present inventive method;

[0027] FIG. 3 is a simulated environment, including a digital life form,
according to the presently described embodiment of the invention;

[0029] FIG. 5 is a flow chart depicting a simulated feeling, as shown in
FIG. 4;

[0030] FIG. 6 is a flow diagram depicting an example of a method for
creating a simulated consciousness;

[0031] FIG. 7 is a diagrammatic representation of a hierarchical process
according to the present invention; and

[0032] FIG. 8 is a diagrammatic representation of a concept chain such as
might be formed according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0033] While this invention is described in terms of modes for achieving
this invention's objectives, it will be appreciated by those skilled in
the art that variations may be accomplished in view of these teachings
without deviating from the spirit or scope of the present invention. For
example, the present invention may be implemented using any combination
of computer programming software, firmware or hardware. As a preparatory
step to practicing the invention or constructing an apparatus according
to the invention, the computer programming code (whether software or
firmware) according to the invention will typically be stored in one or
more machine readable storage devices such as fixed (hard) drives,
diskettes, optical disks, magnetic tape, semiconductor memories such as
ROMs, PROMs, etc., thereby making an article of manufacture in accordance
with the invention. The article of manufacture containing the computer
programming code is used by either executing the code directly from the
storage device, by copying the code from the storage device into another
storage device such as a hard disk, RAM, etc. or by transmitting the code
on a network for remote execution. The method form of the invention may
be practiced by combining one or more machine readable storage devices
containing the code according to the present invention with appropriate
standard computer hardware to execute the code contained therein. An
apparatus for practicing the invention could be one or more computers and
storage systems containing or having network access to computer
program(s) coded in accordance with the invention.

[0034] A presently known mode for carrying out the invention is a computer
program, operative on a general purpose computer, for accomplishing the
inventive method as described herein. An example of an inventive
simulated awareness method is depicted in a flow diagram in FIG. 1 and is
designated therein by the general reference character 10. FIG. 2 is a
block diagram of a computer system 12 such as is anticipated to be used
to accomplish the simulated consciousness method 10. Illustrated is a
general purpose computer 14, having the usual appendages such as a
keyboard 16, a pointing device 18 (generally a mouse), a display screen
20, a printer 21, a removable medium 22 (such as a floppy disk or CD ROM)
in a removable medium drive 24, and a fixed medium drive 26. The
inventive simulated awareness method 10 will generally be stored upon the
removable medium 22 for downloading into the fixed medium drive 26 of the
computer system 12. In addition, a data base 28 consisting of data to be
used with the present inventive method will generally be stored on the
fixed medium 26.

[0035] According to the present inventive method, goal directed behavior
is used to simulate the sort of response usually associated with a
conscious being. A primary goal is the "survival" of a digital life form
("DLF"). A diagrammatic representation of a simulated environment 30
including a DLF 32 is depicted in the view of FIG. 3 and will be
discussed in greater detail, hereinafter.

[0036] In the presently described example of the invention, the "life" of
the DLF 32 is represented numerically in the computer system 12. This
simple concept will be familiar to those practiced in the art of computer
games, wherein a numerical score is used to represent the relative
vitality of a character. However, an essential difference here is that
the vitality of the DLF is maintained by the actions of the DLF itself,
and as such it is a conditional entity.

[0037] Referring again to FIG. 1, the simulated awareness method 10
functions as an endless loop (with exceptions as discussed hereinafter)
wherein an action 40 attempts to achieve a goal 42 which, if successful,
as determined in a success decision operation 44, will result in the
survival 46 of the DLF 32 (FIG. 3). Subsequently, another action 40 is
selected in a select action operation 48, and an experience tally 50 is
incremented. These operations will be discussed in more detail
hereinafter. As can be seen in the view of FIG. 1, should the action 40
not be successful (or alternatively, should successive actions not be
successful, as will be discussed hereinafter), then the DLF 32 is
deactivated, simulating the "death" 52 of the DLF 32, as consistent with
its conditional nature.

[0038] The present inventive DLF 32, as with any life form, must cause its
own future existence (survival) precisely because it is a goal directed
or internally driven entity, as opposed to a rock, which is not goal
directed. Any actions of a rock are simply the result of outside forces.
Failure to maintain goals causes the life form to cease to exist, a
condition in which it is no longer part of reality and one that is
irreversible. Only behaviors that are successful will be repeated in the
long run, as will be discussed in more detail, hereinafter.

[0039] Prior to the present invention, there has been a profound and
fundamental difference between state of the art computer systems and
biological life forms, between mechanical/logical systems and
teleological systems. If something is a real life form, that is, if it is
alive, it must be conditional, because that is the essential attribute of
all life forms. The artificial life form (DLF 32) should therefore be
goal directed, which means that it be internally driven by its own
values, energy source, internal locus of control, and the value
significance to itself of its own values (what it needs to stay alive).
In order to act according to the present inventive method, the DLF 32
should have values (or an equivalent thereof) and act to gain and keep
them on its own power. Simulated death is the primary means this
invention uses to solve the problem of the apparent need to predefine a
simulated life form's future actions. Simulated death solves this problem
because only pro-life actions get repeated in the long run. DLFs 32 that
fail in pro-life actions or attempt anti-life actions cease to exist and
therefore can no longer act. Thus they have no long term causal
significance in the simulation.

[0040] Referring again to FIG. 3, it can be seen that in the simulated
environment 30 the DLF 32 exists along with a plurality of external
objects 60. These objects could represent things such as food 62, which
would contribute to the viability of the DLF 32. Another example would be
that an object 63 could represent a threat to the DLF 32 if the DLF 32
does not take action to avoid it.

[0041] According to the present inventive method, a DLF 32, just like a
living organism, must take in materials and energy from the environment
30, and it must use the appropriate materials and energy for self
maintenance, self repair and self reproduction. And, also like a living
organism, once the DLF 32 has died, it cannot be reconstituted--failure
is irreversible. In order for a DLF to appear to have consciousness, its
primary purpose cannot be to achieve human goals, which is how
conditional programming structures are used in all state of the art
computer programs, but the goals of the DLFs 32 themselves.

[0042] This means that DLFs 32 must be logically structured to take action
to maintain their existence, and that they must be deleted if their
survival actions fail.

[0043] Accordingly, the DLFs 32 must be equipped with a pallet of
potential actions 64 through which it can interact with the objects 60 in
its environment 30. Human programmers can predefine basic actions such as
look, find, or eat, to build a starter simulation system goal directed
action refers to actions (or sequences of basic actions) selected by a
life form for survival purposes.

[0059] A programmer skilled in object oriented programming can make
simulated feelings attributes of a class of DLF 32 program objects. The
simulated feelings give the DLF 32 an instantaneous indication of its
life status, and, if put into a window on the computer screen as part of
a DLF 32 program interface, a human observer can see the same status. By
being conscious of its own life status, a DLF 32 can take actions to
cause its future survival, since it would have the information that is a
prerequisite to such actions. Simulated feelings are the simplest form of
simulated self awareness or self consciousness, though at this level a
DLF 32 is not aware that it is aware of itself.

[0060] As can be seen from the example above and that of FIG. 3, a DLF 32
can have attributes 65 such as a quantity of energy packets ("EPs") 66
which represent its degree of vitality. When a DLF 32 reaches zero EPs
66, its life would end. Therefore, maintaining an adequate energy supply
(sufficient EPs 66) becomes the basis for all other actions a DLF 32 may
be capable of performing. Therefore, once the DLF 32 programming object
has been created and defined, processes called methods (object oriented
computer programming code) must be defined to enable the DLF 32 to take
action and an action selection method to enable internal control of its
actions to find simulated food in its simulated environment to generate
more EPs 66. This must be a continuous process to enable the DLF 32 to
survive, just like a biological life form. These methods define the
actions 64 depicted in the view of FIG. 3.

[0061] As seen in the view of FIG. 3, the DLF 32 can include one or more
percepts 67. As defined herein, a percept 67 is a list of the perceived
characteristics of the objects 60 that is calculated from input sensed by
the DLF 32 from the objects 60 in its environment 30. Each percept 67 is
a list of the properties and values (property measurements) of a
corresponding object 60. To the DLF 32, the percepts 67 are the
identities of the objects 60. Therefore, the percepts 67 are the
processing units of simulated perceptual consciousness in a DLF 32, as
will be discussed in more detail hereinafter.

[0062] As with the DLF 32 program object itself, the program objects 60 in
the DLF's 32 simulated environment 30 must be created and defined (to
save resources and make the system simpler during initial development),
but since these objects 60 are non-conditional (non-living), most need
few action methods for simple reality simulations. More complex and
sophisticated simulated environments (not shown) in which non-living
objects are animated (or contain other DLFs 32), would however, require
coding extensive action methods for those objects. For this reason, at
some point in the development of the system, it will become desirable to
use objects sensed in the real world as with TV cameras, microphones,
pressure sensors, and the like, to eliminate the need for such extra
program coding and put the DLFs to practical real world use.

[0063] By way of example, the program code for an "Eat" method 69 can
automatically include digestion, generating energy EPs 66, and the
simulated feeling of being "full". The code for a "Stop" method, in this
example, is a simple loop that continuously tests for feeling of
fullness, and stops the Eat method when that condition is met. The code
for the Death 52 method erases the current DLF 32 from the computer's
memory and calls the Birth method which increments the DLF 32 name
attribute by one and resets the other attributes to initial conditions.

[0064] It will be advantageous to save pro-life behaviors and maintain
them between generations of the DLFs. This may be done by not erasing the
behaviors from memory at simulated death 52, thereby simulating genetic
evolution to carry the behaviors forward to the next generation of DLFs
32. Alternatively, some other method not yet contemplated by the inventor
might be used for this purpose. In any event, it is important that the
only actions that get repeated long term are the valuable actions. Life
forms (DLFs 32) that repeat any other kind of actions simply get wiped
out and no longer exist, and only actions of those DLFs 32 that are
relatively successful should be carried forward to subsequent
generations.

[0065] The complexity of the program code for sensing the environment 30
will differ greatly depending on whether the environment for a DLF 32 is
simulated or real. The two types of environment are essentially
equivalent, except that real sensors sensing reality provide much more
accurate and detailed real time data of the world, whereas simulated
worlds are limited to human imagination and computing resources.
Simulated environments are primarily useful for developing, testing, and
proving program methods while conserving resources. Sophisticated
simulations intended for practical uses will need to interact with the
real world to be effective.

[0066] An example of a slightly more complex simulated consciousness
method 10a is depicted in the view of FIG. 4. In a perceive environment
operation 70 objects 62 in the environment 30 are located and then
identified. In this simple example, the only objects 60 of interest are
food 62. If food is not found, a check is made to determine if there are
sufficient EPs 66 to maintain existence. If not, the death 52 operation
is called, wherein the DLF 32 is deleted from memory and a birth
operation 72 is called to create a new DLF 32. If there are EPs 66 to
continue, the loop returns to the perceive environment operation 70. When
food 62 is identified, the program proceeds to an eat operation 74
wherein the food 62 is assimilated and used to create EPs 66. This
process is continued until there is no more food 62 immediately available
or else until the DLF 32 is "full"--that is, until it has achieved its
maximum quantity of EPs 66.

[0067] As can be appreciated in light of the above discussion and the flow
diagram of FIG. 4, once various objects 60 have been perceived by a DLF
32, they must be evaluated with the DLF's 32 life as the standard of
value. To a biological life form, since its continued existence is
conditional, every percept is either a value or a disvalue relative to
its life. That is, every percept has value significance to the life form
as being information about its world that is either for or against its
life. In order for a DLF 32 to be an accurate simulation of a life form,
therefore, a DLF 32 should also be able to determine the value
significance of its percepts 67 (FIG. 3). One way to accomplish this is
to simulate pleasure and/or pain, in much the same way the other
generally biological functions have been simulated as discussed
previously herein. For example, in FIG. 4, a "feeling" operation 76
calculates whether or not the DLF 32 is experiencing the feeling of being
"full". In like manner other feelings can be simulated.

[0068] The pleasure/pain systems of biological life forms are automatic,
built in value systems. In general, things that are good for a life form
cause it to feel pleasure, and things that are bad for it cause it pain
(either physical, emotional, or both). In order to create a digital
simulation of a life form a similar automatic, built in evaluation system
is desirable and, like actions, this can be copied from biological life
forms and predefined so evolution does not have to be recapitulated by
DLFs 32. Since computers are not biological, simulated pleasure and pain
must be calculated based on simulated values which serve as standards
with the life of a DLF 32 being the ultimate standard. The ideal is to
make simulated evaluations as causally and functionally equivalent to the
biological ones as is technically possible. An example of a flow chart
for calculating a simulated feeling is depicted in the view of FIG. 5.
For example, to calculate if the DLF 32 feels "full" in computational
terms, a method is written that compares the number of EPs 66 that a DLF
32 has with the range that its simulated life requires. Having EPs 66 is
a value to a DLF's 32 life; without them the DLF 32 will die just as a
biological life form will die without food. A simulated feeling 76 can be
calculated for any number of EPs 66 a DLF 32 has at any specific time by
comparing the number it actually has to its required range. As can be
seen in the view of FIG. 5, in this example, the feeling 76 is calculated
by a getting current EPs operation 80, then a comparing value operation
82 wherein the current EPs 66 are compared to a set range of acceptable
values, then the feeling 76 is calculated in a calculate feeling
operation 84, based upon where the current EP 66 quantity lies within
this spectrum. Finally, the calculated feeling 76 is stored as an
attribute of the DLF 32 in a store attribute operation 86. The attributes
of the DLF 32 are discussed above and in relation to FIG. 3.

[0069] Early in a DLF's 32 life, when there are few examples of percepts
67 and how the DLF's 32 previous actions changed them, most of the DLF's
32 actions will be selected by trial and error. However after an extended
life and, perhaps, many thousands of perception/action events, the action
selection methods will have much more data to use and will, therefore, be
able to select actions with the greatest survival value more efficiently.
The operating principle here is that identity (processed data in the
"memory" of a DLF 32) determines action capacity. As the amount of data
increases in the DLF the identity of the DLF effectively changes in a way
that increases its action capacity, just as occurs in biological life
forms to varying degrees. Some examples of action strategies that might
be provided by a programmer are as follows:

[0070] Continue the last action: This is a useful strategy when an action
is succeeding in improving simulated feelings (such as eating to reduce
hunger).

Select the action that resulted in pleasure in the past when a given
object was perceived: This option is similar to the previous one, but is
recalled from a memory association from the past.

[0071] Select no action: This is a useful option when all simulated
feelings are positive and no action is required to change them. It is
also an example of an optional action. Follow a pre-programmed process
(when a given object is perceived, as with instinctual behavior in
biological life forms such as nest building, or habits in humans): This
option is a good strategy for a goal requiring complex actions or series
of actions.

Random action selection: This option is analogous to trial and error
actions observed in biological life forms and is useful for new
situations when no other action gets selected. It is another example of
an optional action.

[0072] According to the present invention, actions are not preselected,
but rather are selected by simulating the perceptual consciousness
process and, as with its biological counter-part, this process is an
automatic one (in the teleological sense). There is no other basis for
making selections because options are limited at the perceptual level of
awareness to the objects in the DLF's 32 world and the DLF's 32 own
simulated pleasure-pain responses to those objects. However, action
selection is teleological because its goal is a DLF's 32 survival, the
DLF's 32 simulated life is the standard of value and it, therefore,
cannot be explained as simple mechanistic, billiard ball type of
causality.

[0073] When creating action selection methods the following points should
be considered. An action selection method should insure that some action
is always selected for any perceptual event (even a "no action" method is
an action in this context). An action selection method should be
teleological in that its goal is causing the survival of the DLF 32 with
its simulated life as the standard value, and does so by increasing the
DLF's 32 simulated happiness. Only survival actions get repeated in the
long run. "No_Act" and/or "Random_Act" methods can allow for a DLF 32 to
maintain its simulated happiness for a time, provide for trial and error
actions, and allow for the unexpected or the novel event to be simulated.

[0074] It will be recognized by one skilled in the art that after a great
many "experiences" by the DLF there will accumulate a great deal of data.
Therefore, it may be desirable to divide the data base 28 (FIG. 2) to
have both short and long term storage wherein much of duplicate short
term information is deleted

[0075] FIG. 6 is a flow diagram depicting an example of a process 90 for
creating a simulated consciousness method 10. In the example of FIG. 6,
it can be seen that in a define DLF operation 92 the attributes 65 for a
DLF 32 are determined and defined, and provision is made to store such in
the data base 28 of the computer 14. Again, one skilled in the art of
object oriented programming will appreciate that this is a relatively
simple process. In a provide access to environment operation 94 provision
is made for allowing the DLF 32 to perceive its environment 30. As
discussed previously, herein, the nature of this operation will vary
according to the nature and complexity of the environment 30. If the
environment 30 is entirely simulated, as in the simple example of FIG. 3,
then the programmer can merely define the objects 60 in the environment
30 as program objects. Alternatively, if the DLF 32 were to be intended
to operate in the "real world" then sensors could be provided to sense
real world objects (not shown) and identify them. The technology for this
currently exists and is being further developed, and is not an aspect of
this particular invention.

[0076] In a provide selection of actions operation 96, a programmer will
define selected actions 64, as previously discussed herein, and will
further define the circumstances under which particular actions 64 will
be selected. In a define consequences operation 98, the programmer will
provide for the simulated feelings 76 which will assist in determining
the appropriate action 64. Also, as previously discussed herein, the
consequence of simulated death 52 and birth will be programmed.

[0077] FIG. 7 is a diagrammatic representation of a hierarchical process
100 such as will enable a DLF 32 to achieve simulated consciousness. As
can be seen in the view of FIG. 1, the DLF 32 will first form percepts 67
in a percept formation operation 102 such as has been discussed
previously herein. It should be noted that many percepts 67 will be
created, essentially one for each object 60 or entity encountered in the
environment 30 of the DLF 32, and these percepts 67 are the identity of
that object 60 (properties and measurements). Therefore, the diagram of
FIG. 7 is not a flow diagram, but rather a hierarchical diagram showing
the levels of operation of the DLF. As can be appreciated by one skilled
in the art the percept formation operation 102 will be repeated, as
necessary, as objects 60 are encountered in the environment 30.

[0078] FIG. 8 is an example of a concept chain 104, which will be
discussed hereinafter in relation to the remainder of FIG. 7. When the
DLF 32 has stored sufficient percepts 67 to make comparisons, a concept
106 can be formed by such comparison. For example, any shapes which are
closed, and comprised of three straight sides and three corners can be
grouped together to form a concept 106 "triangle". When sufficient
concepts 106 have been formed for comparison, these can be compared to
make additional concepts 106. In the example of FIG. 8 it can be seen
that the concepts 106 "triangle", "circle" and "square" have similar
characteristics which can be grouped under the concept 106 "closed
shape". In like manner, the entire concept chain 104 of FIG. 8 can be
formed, given sufficient experience by the DLF 32. Higher and lower level
(more abstract) concepts 106 are formed by comparing the attributes of
other concepts 106, as can be seen in the view of FIG. 8. This means both
more general and more specific concepts 106 can be formed at any point in
the mid levels of the hierarchy of concepts. Concepts 106 can therefore
identify any kind of relationship between percepts 67, and at all levels
of complexity, but they all must be connected by unbroken chains to
perceived objects in the DLF's 32 world at some point.

[0079] It should be noted that concepts 106 start being formed by
comparison of certain particular attributes of percepts 67. For example,
looking only at the relative position of objects 60 can lead to the
formation of concepts such as "above", "to the right of" and the like.
Likewise, concepts can be formed relating to intangibles. That is,
concepts are calculated for objects, actions, relationships and even for
other concepts.

[0080] Referring again to FIG. 7, it can now be appreciated that a next
level of operation of the DLF 32 following percept formation 102 will be
concept formation 108, wherein concepts 106 are formed, as discussed
above. One skilled in the art will recognize that concept formation 108
will not be an inherent characteristic of a DLF 32, but rather will be
provided for as one of the actions 40 available to the DLF (much like
"eat", or the like), which have been discussed previously herein and may
require multiple simulated conscious loop cycles to complete.

[0081] It should be noted that the formation of concepts 106 does not
inherently provide for a name for the concepts such as have been used to
discuss the example of FIG. 8. That is, just because the DLF 32
recognizes the similarities between objects such that it can group all
triangular shaped objects together by such similar characteristics, that
does not mean that the DLF will understand that these are called
"triangles" in English or by some other name in other languages. The
concept 106 without a word associated (its name) may be referred to as an
"implicit concept", wherein the DLF 32 has the data to form a concept,
but does not yet have a name for it. It is a real, workable data
structure in the system, but not yet linked by association to the DLF's
32 symbol system. As discussed above, concept formation is a form of
simulated volitional (free-will, or optional) behavior. Percepts 67 are
calculated automatically (in a teleological, not mechanistic sense).
Concepts 106, however, are calculated only as optional behavior, this
being non-automatic action. (Optional behavior consists of actions a DLF
32 can perform if and only if its necessary actions such as eating have
been completed successfully, thus ensuring it has sufficient EPs 66 to
stay alive. This is necessary because there are a unlimited number of
potential concepts 106, and a DLF 32 could actually die by "thinking" too
much.) But what the computer 12 cannot do on its own is to come up with a
real language word. The computer 12 could come up with its own word, but
then it would have to be translated in order for the computer 12 to
communicate with people in the real world. In order to provide real
English word, a human tutor should interact with the DLF much like a
child would learn. The ability to decode and encode sentences depends on
both words and concepts, because the chains of concepts 106 connecting
them to percepts 67 is the meaning of the words. The DLF 32 will perceive
words as objects 60 and can form concepts 106 of both objects 60 and
their relationships, as well as sentences and the parts of sentences.
(The sentences themselves are just another form of perceptual object 60
in this system.) This process is represented diagrammatically in a word
association operation 110 in FIG. 7, wherein a concept 106 is associated
with a word, as discussed above. Once concepts 106 are formed, as shown
in FIG. 7, the encoding of a sentence may follow. This is a process that
starts with objects 60 and connects them to the words that make up the
sentence. Reversing the arrows would be the decoding of a sentence,
essentially by reconnecting the words in the sentence to objects 60 in
reality. Both processes operate by tracing previously calculated
conceptual chains 112, or in some cases, by calculating new ones. In the
view of FIG. 8 it can be seen that each of the concepts 106 is
represented by a natural language word 112 in the concept chain 112.
Simulated perception, concept formation, and the processes of encoding
and decoding sentences, taken together as described herein, solve the
problem known in the state of the art as natural language understanding
and production. It should be noted that, as discussed above, the DLF 32
can form concepts on its own without human intervention as one of its
optional actions 40. The human contact is only required to enable the use
of natural languages, as described.

[0082] Various modifications may be made to the invention without altering
its value or scope. For example, alternative or additional actions,
methods, and the like might be used instead of or combined with those
described herein. One additional action method could be the ability to
compare a wider variety of characteristics of the objects 60 a DLF 32
perceives, to make the DLF 32 better able to group and abstract percepts
by similarity. Another example of an obvious modification would be to
incorporate the invention into a robotic device or other such machine
that better simulates human sensors, brain, and the ability to identify
objects in the real world, instead of the general purpose computer used
in the example of this disclosure.

[0083] All of the above are only some of the examples of available
embodiments of the present invention. Those skilled in the art will
readily observe that numerous other modifications and alterations may be
made without departing from the spirit and scope of the invention.
Accordingly, the disclosure herein is not intended as limiting and the
appended claims are to be interpreted as encompassing the entire scope of
the invention.

INDUSTRIAL APPLICABILITY

[0084] The inventive simulated awareness methods 10 and 10a are intended
to be used in ever increasingly complex forms to eventually result in a
DLF 32 which can interact with humans and the "real" world in which we
live, thereby resulting in a program which appears to have consciousness
and which can solve problems for which it is not specifically programmed.

[0085] A relatively short development time is provided for, since this
invention copies many design ideas from real life forms, instead of
attempting to re-evolve them to recapitulate evolution in some manner,
such as is attempted by genetic algorithms, and the like. In other words,
just as the AL researchers did not re-evolve the gait of insect robots,
but rather reverse engineered their operation by copying real life forms,
so this invention seeks to reverse engineer the simulation of goal
directed behavior and consciousness rather than re-evolve it. A key is to
identify the essential elements and program substitutions. This is the
pre-defined part of the simulation system. This aspect of the design
involves identifying the necessary and sufficient set of elements to
develop the substitutions for, and then writing the software code for
those elements. This self defining stage of the development of the
simulation system is the management and tutoring of those basic elements
as they simulate the active processes of life and consciousness.

[0086] The inventive method can be practiced using a reasonably powerful
desktop computer with at least 64 MB of memory, a 1 GB hard disk drive,
and an object oriented programming environment to write a goal directed
program that simulates a life form. Writing a program to simulate goal
directed behavior on the computer system 12 amounts to creating the DLF
32 and the simulated environment 30 in which the DLF 32 will live.

[0087] Simple simulations involving a few thousands of percepts 67 would
require less computer resources and could be done on a high end PC, but
complex simulations of higher life forms that involve millions of
percepts 67 for natural language understanding could require a more
powerful computer system, such as those used for large Internet servers.

[0088] A simulated or virtual environment can be made very sophisticated
and is easier and less expensive than using a real one, because it can
exist entirely in a computer's memory, so no external sensors or
actuators are needed. To simulate high order functions such as rational
consciousness accurately, a DLF 32 will eventually have to interact with
the same world that human beings do, including interaction with people.
However, simulations of simpler DLFs 32 do not require real world
contact. Both simple and complex simulations that use external robot
technologies are possible with today's technology, and will become even
more realistic in the technical improvements that will come in the near
future.

[0089] The present invention is based, in part, on the concept that
knowledge, in order to be objective, has to be connected to reality (what
is perceived). Every object has an identity which is unique, objects
interact with one another. This is referred to as causality. Causality is
not merely one event following another. Rather, interaction of the
identities of objects is causality. There are essentially two types of
objects--non living and living. Non living objects are totally externally
driven. They exist unconditionally, whereas living objects exist
conditionally. Certain actions they must take or they die and cease to
exist. This makes them a different kind of entity, with different kind of
causality. A DLF 32, according to the present invention, like a real life
form, can have optional behavior. Once the DLF 32 has satisfied survival
needs, it is free to do what it wants. It can engage in more survival
action, can do nothing, do random actions, or the like. Alternatively,
like human beings, the DLF 32 can form concepts 106--it can look around
the world and learn. In both DLFs 32 and humans there are two types of
behavior. The first of which is necessitated for survival which, even
though "automatic" in a goal directed, biological sense, is different
from that of mechanistic automatons, because it is teleological. To be
biological is to be internally powered and regulated.

[0090] Since the life form, simulated or real, must maintain its survival
by internal self regulation, it must take such necessitated actions and,
in order to do so it must be able to see (or sense) its environment to
identify objects that exist there and predict likely outcomes of its
actions (set goals). There will be a survival advantage in taking raw
data and integrating it into percepts 67 and then into concepts 106,
since the DLF 32 will be able to learn from its experience thereby, and
since concepts will allow the DLF 32 to act based on generalities, and
the like, thereby reducing the number of calculations required because
percepts and concepts are stable, invariant condensations of what is
sensed in a highly dynamic outside world. As discussed previously herein,
concept formation is among the second, optional, types of behavior which,
while not immediately necessary for survival, might well enhance the
likelihood of survival of the DLF 32 in the long run (as it does in fact
for humans). Because such optional behaviors must be planned for the DLF
32 by the programmer, they will be limited in quantity as compared to a
nearly infinite variety of possible (and emergent) optional behaviors
that will be possible once the DLF 32 has formed a multitude of concepts
106. However, as discussed herein, it is important that the DLF does have
at least some such optional behaviors from the start.

[0091] It should be noted that the DLFs differ significantly from what
many seem to consider to be "life forms". That is, it would not be
correct to lump real life-forms, computers, life-forms simulated
primarily to attain human goals, and life-forms simulated to primarily
attain their own goals all into the same category. Although this present
invention may "look" like a state of the art simulation of life, such as
is represented by Heleno et al, "Artifical Animals in Virtual
Ecosystems", published in Computer Networks and ISDN Systems, Volume 30,
Issues 20-21, November 1998, pages 1923-1932, such a comparison would not
be valid due to significant differences. The same is true for sense
perception (which is often incorrectly lumped in with sensations and
bitmaps), and for concept formation (which nearly everyone is taught is
simply a case of either intuition or of making up a definition base on a
rational that can be sold to enough other people to get accepted).
Concept formation is almost never taught as a quasi-mathematical method
that is based on direct observation of reality as it is used in this
invention.

[0092] Computer systems, including so-called "autonomous agents" are
mechanistic, whereas life-forms are teleological, or goal directed in the
sense of being self-regulating. The system described by Heleno et al.,
for example, is a study and educational tool created to provide "dynamic
pictorial information" to people who then find it is "easily interpreted
by a human being and clarifies the behavior of the interactions." The
so-called "goals" of the animals in the model are merely variables that
make it easier for scientists to have a "reproduction of natural
phenomena" in their labs. They are an artifact of a subject being
studied. They are not the "goals" of the actual life-forms to be used for
the life-forms' own survival purposes. If the system described by Heleno
et al. were used to study the behavior of small fires spreading and
joining into an inferno, the subject of goals would not be involved, for
fires are not life-forms. Yet it would work all the same, because Heleno
et al. makes no distinction between mechanism and teleology. That is,
simulations such as described by Heleno et al. assume that life is
inherently mechanistic. Similarly, simulations designed to display
behavior, such as the popular "artificial pet" simulations, while useful
for their own purposes, are not comparable to the present invention. The
inventive DLF system is much broader and more complex because it can
simulate life forms in general. In addition, such machine simulations are
a "machine" by definition, which means that they are non-teleological by
definition, and are therefore incapable of goal-directed action for its
own sake. Moreover, in such simulations the relationship of the machine
to a human being is one of being a "user." By inference, therefore, an
"electronic pet machine" or the like, has no goals that it acts to
achieve for its own sake, but like the animals in the Heleno et al.
system, they are merely a tool to help its human "user" achieve human
goals. By comparison, the inventive DLF system will be used by humans to
help achieve human goals in the same sense that real animals such as
horses are, because DLFs (like real horses) are teleological, DLFs will
primarily act to achieve their own goals and only secondarily act to help
achieve human goals. This point also applies to the question of simulated
feelings. These simulated feelings do not exist for the sake of the pet
machine itself, they exist only for the sake of their effect on the human
user. Whereas, the simulated feelings of pleasure and pain in the DFL
system function primarily as warning indicators for the life status of
the DLFs themselves, and only secondarily as for human users.

The distinction between the mechanistic and teleological action is not
mere semantics. It has been studied and argued extensively in the
literature. Non-living objects exist unconditionally, they function by
simple mechanistic or "billiard ball" causality, and they do not act to
maintain their existence. For biological life-forms their very existence
is conditional: They must act to remain alive and in existence, in fact,
to cause their own future existence. Moreover, they must act in very
specific ways. A more complex causal explanation is required than simple
mechanistic causality provides. Therefore, another important difference
is that this present invention simulates the more complex form of
causality that makes biology possible at all, whereas simulations of life
forms in Heleno et al, and the like do not.

[0093] The inventive DLF system rests on the premise that computer
technology cannot have the attributes of a life-form, such as conscious
behaviors, based on a purely mechanistic design. Artificial Intelligence
(AI) and Artificial Life (AL) design strategies are oversimplifications
of the reality of life processes because they ignore teleology
(goal-directed action), except insofar as it furthers human goals. Thus
the state of the art design strategies preclude digital life-forms that
live for their own sake from the outset. All state of the art systems
known to the inventor exist for the sole purpose of satisfying human
goals, not goals of the simulated life-forms they mimic.

[0094] If the purpose of simulating hunger in the inventive DLF system
were simply to create a pet simulation machine it might be comparable to
the appearance to humans of a "pet machine", or the like, taking action
to satisfy hunger or of behaviors that make the pet machine appear full
or to have more energy after eating. But that is not the purpose of
simulating hunger in the inventive DLF system. Rather the purpose is to
simulate an active metabolism in a conditionally existing DLF for the
ultimate purpose of simulating teleological goal causation. In other
words, a simulated "pet machine" might eat and later look satisfied and
have more energy because it is scripted to satisfy the observational
needs of the human user. Alternatively, the inventive DLF eats because it
is simulating hunger in the manner of a real animal, and is driven by its
pain system to do so with the goal of causing its own survival as seen
from its own internal perspective.

[0095] Similarly, prior art AI systems, and the like, are designed to be
an easy to use modeling and study tool or a pet machine, but not to
simulate the actual teleological causation of biology as is the inventive
DLF system. In known prior art systems, all the functions are clearly
designed for use by humans, not for the animals or pet machine to
identify reality for their own sake, for their own goals, and from their
own perspective. The emphasis here is clearly on the ease of use of the
system for human users (scientists, students, pet machine owners, etc.),
not the value of sense perceptions of the actual animals in the
simulation itself from the animals' own perspective. Whereas, in the
inventive DLF system, the whole purpose of having sense perceptions in
the first place is to enable DLFs to gain the identity information about
objects in the outside world so that DLFs can survive, because they need
to identify reality so that they can take action to cause their own
future existence, as opposed to help some scientist write his papers or
play with an artificial pet.

[0096] Sense perceptions in AI simulation type machines, as is typical in
the known state of the art, are simply bitmaps and quantities in
variables. However, in the inventive DLF system sense perceptions are
computed from bitmaps or variables while using them as data, but are not
bitmaps themselves. From the perspective of DLFs, these sense perceptions
ARE objects in the world, not quantities in variables. Moreover,
consciousness is simulated using this more complex perceptual data type.
In the DLF demonstration program embodiments created to date, these
computed percepts are simple lists, but as they function in the DLF
system they are a data type that is new to the art and designed
specifically for this purpose. Other non-list based future designs are
possible (such as more complex perceptual forms of objects produced by a
Reality Identification Cortex that better simulates the human brain), but
the point is that simulated consciousness of DLFs will not be "conscious"
of a database of simple variables or bitmaps, they will be "conscious"
(from their perspective inside the system) of percepts that consist of a
foreground of objects against a background of other objects, real world
objects that have real characteristics or features that make up their
identities, such as those that people can and do perceive when they look
at trees and rocks and cars and other people or hear words spoken as
"sound objects," not as text strings consisting of bits. In DLF simulated
percepts, each characteristic or feature in these multi-feature data
objects will consist of an invariant property and a value measurement
that can then be used in later computations, especially those used for
forming (calculating) concepts according to a specific,
quasi-mathematical method.

[0097] In the state of the art, consciousness is defined as either various
kinds of spiritual mysticism or in science as a transparent
eppi-phenomenon with no identity of its own. Ayn Rand was the first
person to recognize consciousness as a process of awareness when she said
in her book Introduction to Objectivist Epistemology: "Consciousness IS
Identification." That is, identification done by a life-form from the
perspective of the conscious life-form doing the identifying, not some
outside user. By "identification," she means identification of objects,
the ordinary things everyone sees in the world. By process, she means an
identify-able series of steps like any other process that is performed by
the brains of certain life-forms. In addition, she defines "percepts" as
the output of this process and as "a group of sensations automatically
retained and integrated by the brain of a living organism." (Note: The
term "automatic" is used here in the context of biological,
goal-directed, automatic behavior of life-forms, not the computational
idea of mechanistic automatons.) The DLF system is designed to animate
the process of conscious perception in simulation, as it was conceived by
Ayn Rand by substituting the mechanisms of a computer system for the
mechanisms of physics and chemistry that animate life-forms, with special
programming added to simulate goal directed behavior. In the DLF system,
"sensations" are analogous to bitmaps or variable definitions in prior
art systems, but they are simply used as a starting point and as data.
There is then additional processing to simulate the "integration,
retention, and identification" parts of Rand's process explanation of how
consciousness works in life-forms. This design is very different from the
"sense perceptions" in Heleno are intended to model virtual ecosystems
and produce: "dynamic pictorial information" . . . that provides a
"visualization of a natural environment in a VR system."

[0098] Since the simulated consciousness method 10 of the present
invention may be readily produced and integrated with existing computer
systems and sensing devices, and the like, and since the advantages as
described herein are provided, it is expected that it will be readily
accepted in the industry. For these and other reasons, it is expected
that the utility and industrial applicability of the invention will be
both significant in scope and long-lasting in duration.